code: def train_model(model, dataloader, num_epochs, optimizer, loss_fn):
      for epoch in range(num_epochs):
          running_loss = 0.0
          running_loss_count = 0
          for batch_idx, (inputs, targets) in enumerate(dataloader):
              inputs = inputs.cuda(non_blocking=True)
              targets = targets.cuda(non_blocking=True)
  
              # Forward pass
              outputs = model(inputs)
              loss = loss_fn(outputs, targets)
  
              # Backward and optimize
              optimizer.zero_grad()
              loss.backward()
              optimizer.step()
  
              running_loss += loss.item()
              running_loss_count += 1
  
          # Print loss statistics
          print(
              f"Epoch {epoch + 1}/{num_epochs}, "
              f"Loss: {running_loss / running_loss_count}"
          )
  
file_path: keras/guides/distributed_training_with_torch.py
imports: import os
  import torch
  import numpy
  import keras
label_desc: Use image node_id 3 for calling a global function with example usage: train_model(model, dataloader, num_epochs, optimizer, loss_fn) without return types